In an era of constant, growing accumulation of information quantified numerically, the need for establishing data authenticity also increases. Identifying false, fabricated data within a mass of numbers becomes a challenge that many interested parties strive to overcome, especially investigative bodies, institutions that monitor budget spending reports, and governmental agencies overseeing financial operations in the private sector. Scientific literature suggests using a natural distribution of numbers known as Benford's Law for detecting data manipulation. This study analyzes the numbers in the collected data and their distribution according to Benford's Law. This paper processed two sets of independent data, different in volume and source of collection. The goal is to demonstrate the usefulness of data processing for faster detection of misuse, and the use of Benford's Law is proposed as an effective aid for regulatory institutions.